Agentic AI is reshaping how work gets done. Successful adoption doesn’t start with the technology; rather, it begins with clarity on the outcomes that matter most. Organizations leading in this space define specific business problems to solve, then build the data foundation required to support those goals. From there, they scale deliberately, connecting systems, reducing complexity and enabling intelligent automation where it delivers measurable impact. This outcome-first, data-driven approach is what turns AI from experimentation into enterprise value.
AI as a tool, not a strategy
The agentic-native AI era is here, and Salesforce’s evolution offers a clear example of what that looks like in practice, shifting from a traditional CRM platform to one where AI agents actively participate in business operations alongside employees and customers.
Through Salesforce Agentforce and Data Cloud implementations, we’ve identified several recurring themes in how organizations can best design and operationalize data and AI agents. These help teams accelerate adoption while avoiding costly mistakes.
We recommend organizations first align with the business outcomes and use cases they
want to enable. Once those requirements are defined, teams can identify the specific data elements needed to support them.
Getting started
While every implementation is different, we’ve observed four foundational themes that consistently differentiate successful Agentforce and Data 360 deployments. Organizations that establish these capabilities early are better positioned to scale AI while avoiding common implementation challenges.
Start with business outcomes, not technology
Successful organizations begin with a focused set of high-value use cases tied to measurable business outcomes. Once those priorities are defined, they identify the data required to support them rather than attempting to expose every available dataset to AI.
Build the right data foundation from the beginning
Designing data graphs, selecting the appropriate fields and curating datasets early creates a scalable foundation for AI. These architectural decisions become significantly more difficult and expensive to revisit once agents are in production.
Embed governance and trust into the implementation
AI governance extends beyond security. Organizations should establish PII controls, maintain end-to-end traceability across prompts, topics, actions and flows, and ensure every agent interaction can be audited and explained.
Validate before you scale
Building confidence in AI requires continuous testing and observability. Teams should leverage Agent Builder, audit trails and production-like datasets to evaluate agent behavior, troubleshoot issues and refine performance before expanding into additional use cases.
Scaling for success
Realizing the full potential of agentic AI and Salesforce demands focused execution and changing the way people work. Organizations that successfully scale these capabilities are consistently prioritizing four key areas:
- Aligning AI and agent use cases to high-impact business priorities early, focusing on a targeted set of use cases that improve customer experience, operational efficiency and measurable outcomes.
- Implementing a Headless 360 foundation to enable connected, scalable experiences, leveraging integrated data and APIs to deliver consistent customer interactions without rebuilding core systems.
- Ensuring data is accurate, connected and accessible for AI agents, recognizing that agent performance depends on high-quality, unified data and that siloed environments limit the effectiveness of AI-driven decisions and automation.
- Modernizing incrementally while building long-term scalability, prioritizing high-value use cases first and establishing a flexible foundation that supports continuous innovation and AI readiness.
Success will depend on the ability to connect strategy to execution—building the right foundation, scaling intentionally and enabling teams to fully leverage AI as a driver of sustainable growth.
A practical example: TDX winning solution
At Salesforce’s 2026 TDX (Technical Developer Conference) in San Francisco, Protiviti’s Eleanor Spolyar, Taryn Kelley and Alona Khoroshylova won the event’s Hackathon with their innovative public sector agent based on Salesforce. The team’s success was driven by analytical thinking, problem-solving, collaboration and a willingness to iterate through trial and error, the same qualities teams should focus on as organizations continue navigating an increasingly AI-driven future.
Protiviti’s winning AI use case at the conference demonstrated how nimble organizations can effectively further their AI objectives. The Protiviti-developed agent streamlines service request intake, analyzes historical data to identify trends and recommends preventative actions. By integrating communication tools like Slack and Tableau for real-time analytics, the agent enables teams to coordinate more effectively, track progress in real time and optimize operations end-to-end. Following the hackathon, Protiviti has demonstrated this solution to cities, showcasing how similar AI-driven capabilities can improve service delivery and operational efficiency in the public sector.
Flexible, future-ready environments
These are exciting times for Salesforce developers and the Protiviti Salesforce team. Our goal is to create flexible, future-ready environments that enhance customer experience and improve operational efficiency across the enterprise. We expect organizations that combine strong business alignment, thoughtful governance and active employee engagement to be best positioned to unlock long-term value from AI investments. While AI can accelerate processes and improve efficiency, human expertise, judgment and cross-functional collaboration will continue to play a central role in driving successful outcomes.
To learn more about our Salesforce practice and our award-winning Agentforce solutions, let’s connect.
